System and method for controlling machinery for randomizing and replicating predetermined argonomic input levels

ABSTRACT

A controller is operatively connected to a dispensing system and configured to change the dispensement of an agricultural input from the dispensing system in different predetermined locations within at least one predefined test plot in a management zone of an agricultural field. The predetermined locations have been randomized and replicated for quantifying the agronomic response in a statistically valid manner.

This application claims priority to U.S. Provisional Pat. App. No.62/042,555 filed on Aug. 27, 2014, the contents of which are herebyincorporated by reference herein.

FIELD OF THE INVENTION

This disclosure relates to the management of agricultural plots, andmore specifically, to randomizing and replicating agricultural inputswithin different management zones of an agricultural field andquantifying the agronomic response.

BACKGROUND

As the demand on the food supply increases and the total viable farmlanddecreases, methods and systems are needed that maximize crop yields.Maximum crop yields result in increased production of agriculturalproducts and more value per acre of land. However, the effort inmaximizing crop yields is difficult, time consuming, and costly in partbecause the characteristics of farmland vary from acre to acre. Thisvariance is due to factors such as the conditions of the soil andtopography. Further, an agricultural farm field may include significantacre-to-acre variations in nutrients, quality of crop produced, andultimately crop yield.

The current practice is to prescribe agricultural inputs, such as seedand fertilizer, to the entire agricultural farm field according to theneeds of the most deficient soil, or according to the averagedrequirements of the different soils. The result is that a substantialarea of the field can receive either more or less of the item beingapplied than what the site specific areas can efficiently use to produceagronomic output, resulting in either a significant waste of expensiveag inputs or unrealized yield potential.

Growers and their agronomic advisors can make more accurate inputdecisions with access to more accurate data of site specific agronomicresponses. Agronomic decision making has been driven by a research modelthat involves yield and other observations from small plots with varioustreatments. Examples would be yield by applied nitrogen rates or seedingrate. Such testing suffers from the limitation of being able totranslate the results observed in a small plot at a research farm toproduction fields, which typically have different background conditionsof soils, fertility, management practices, etc.

It would be desirable to develop a system and method to randomize andreplicate agronomic inputs within different management zones of a fieldto measure the agronomic response to an input within several differentcontexts: i) management zone specific, ii) region specific and iii)growing season weather specific.

SUMMARY

A system for applying an agricultural input and harvesting anagricultural output in a management zone is disclosed. A machineincludes a dispensing system for dispensing the agricultural input. Acontroller is operatively connected to the dispensing system andconfigured to change the dispensement of the agricultural input from thedispensing system in different predetermined locations within at leastone predefined test plot in a management zone of an agricultural field.

A method for randomizing and replicating predetermined agriculturalinput levels within a test plot is also disclosed. The method determinesan agricultural input for analysis. At least two application rates forthe agricultural input are defined. A number of replications for the atleast two application rates for the agricultural input is defined.Constraints of a machine that is used for dispensing the agriculturalinput and a machine for harvesting an agricultural output are defined.The application rates for the agricultural input, the number ofreplications for the application rates for the agricultural input, andthe constraints are associated with an agricultural field. At least onetest plot with an area contingent upon the number of application ratesdefined, the number of replications for the at least two applicationrates for the agricultural input, and the equipment constraints isdefined. Locations (area required defined by equipment constraints) forthe application rates for the agricultural inputs are randomly assignedin the test plot. Yield data with an actual agricultural input level inthe treatment area of the test plot is obtained for identifyingagronomic responses to the treatment levels of the agricultural input(suitable for statistical analysis), which can be aggregated withsimilar yield data and treatment levels of an agricultural input fromagricultural plots in other parts of a geographical area.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an overview of a three-tier architecture of a systemmade in accordance with the present disclosure and depicting a userinterface, system logic, a database, and data inputs and outputs.

FIG. 2 illustrates a screenshot for uploading a new agricultural plotinput in the system of FIG. 1.

FIG. 3 illustrates a screenshot for adding a guidance line to theagricultural plot input in the system of FIG. 1.

FIG. 4 illustrates a screenshot with the agricultural farm fieldoverlaid with management zones and target seeding populations in thesystem of FIG. 1.

FIG. 5 illustrates a screenshot for defining the number of replicationsfor each target rate in the test plot within the system of FIG. 1.

FIG. 6 illustrates a screenshot showing the agricultural farm field withdefined test plots of the system of FIG. 1.

FIG. 7 illustrates a screenshot showing the test plots of the system ofFIG. 11 and providing a machine setup file for an agriculture machinefor the agricultural farm field and test plots.

FIG. 8 is an illustration of an agricultural machine carrying out thedescribed and defined testing.

FIG. 8A is a test plot from FIG. 8 showing three treatment levels eachreplicated multiple times within the test plot.

FIG. 9 is a functional block diagram illustrating hardware components ofa controller in an agriculture machine.

DETAILED DESCRIPTION

Disclosed is an integrated and automated system 100 and method to useglobal positioning system (GPS) to control machines in order to placerandomized and replicated agricultural input or treatment levels 114 (Aginputs 114) within at least one management zone of an agricultural farmfield 214. FIG. 8 shows a machine 220 with dispensing system for placingAg inputs 114 in agricultural farm field 214. The types of Ag inputs 114that can be controlled include seeding, irrigation, nitrogen or otherplant/soil amendment, fungicide, herbicide, insecticide, pesticide,growth regulator, or on/off of the same.

FIG. 1 shows system 100 in the form of a process flow chart andaccompanying configuration implementing the disclosed system 100. Anelectronic user interface 102, such as that shown in FIGS. 2-6, is usedto interface with system logic 104 and a centralized database 116 tocollect, store, and process a map of agricultural farm field 214 (Agfield 202), management zone, input parameters, and agronomic outputobservations (e.g., yield data and harvest observations).

System logic 104 includes, without limitation: 1) the process anddevices for generating a management zone overlay; 2) the process anddevices for formulating a grid (oriented with respect to machine travelto optimize execution of test plots); 3) the process and devices foraligning management zones and target input rates within the test plots212; and 4) the process and devices of providing the randomized spatiallocation of each replicated treatment level of Ag inputs 114 within eachtest plot of Ag field 202, with respect to machine capabilities tooptimize execution.

FIG. 1 also shows the interaction between the centralized database 116and system logic 104 where centralized database 116 responds to call forproviding data to system logic 104 for mathematical and graphicalmanipulations, and stores returned values generated by system logic 104.FIG. 1 also shows how centralized database 116 interacts withimportation of external data 108 which can include weather, moisture,and precipitation information from weather service providers.Centralized database 116 also exports machine setup file 120 to amachine 220 for field execution of the placing of Ag inputs 114 atpredetermined levels within each test plot as well as the other areas ofthe Ag field 202.

What follows is a description of the steps in the operation. FIG. 2shows Ag field 202. User interface 102 includes an enroll Ag field 202(as a map) into system 100. Ag field 202 may comprise one or moremanagement zones. A management zone is a sub-region of a field thatexpresses a relatively homogenous combination ofyield-limiting/yield-potential factors for which a single rate of aspecific crop input/cultural practice (e.g., tillage depth) isappropriate. Ag field 202 can have previously defined or set managementzones and target plant populations. This information can come from theprevious years' harvest as well as the current year's agronomy plan inwhich case it is retrieved by system logic 104 from centralized database116 to generate a management zone overlay and then provided to userinterface 102 as a management zone overlay view.

With Ag field 202 enrolled in system 100, machine observation orplanting points 206 from the previous year (see FIG. 3A) or machineguidance lines 204 are inputted at user interface 102 into system 100(while only one machine guidance line 204 is shown those familiar withthis technology will recognize that there are many machine guidancelines in Ag field 202). User interface 102 can be used to identifyintended machine orientation for the Ag input 114 application inquestion either manually by user defined orientation for machine 220,using machine guidance lines 204, or using previous record ofapplication travel (e.g., planting points 206). The areas for testplot(s) 212 are ideally laid out with respect to intended machine travelto optimize execution. Machine input instructions such as tillage depth,planting depth, tillage angle, residue spread width, the number of seedsper area, weight of seeds per area, volume per area, and weight per areacan be provided at user interface 102. FIG. 3A shows an excerpt fromFIG. 3 of the voluminous number of previous year's planting points 206overlaid on Ag field 202 by system logic 104 and displayed to user atuser interface 102.

Once the intended travel path for machine 220 across agricultural farmfield 214 is captured, a processing function occurs in system logic 104where the previously loaded data about agricultural farm field 214(e.g., management zones and target plant populations) are overlaid on agrid 208, as shown in FIG. 4. Grid 208 is shaded/colored with differentplant population profiles that correspond with target populationinformation 210 by system logic 104 and displayed on the right-hand sideof electronic user interface 102. In this case, a rate prescription forAg input 114, such as a seeding population prescription (same rate forthe entire field or variable rate) can be created in advance, for theentire Ag field 202 the user wants to place test plot(s) 212 within. Theprocess creates machine instructions for the entire Ag field 202 thathas been enrolled into system 100, not just the test plot(s) 212, asthat will be required during the field execution step. On the left-handside of electronic user interface 102, as shown in FIG. 5, the user candefine at user interface 102 Ag inputs 114, which corresponds with thenumber of input rates to be tested, i.e. treatment levels. Eachtreatment level for Ag input 114 is replicated three or more times pertest plot 212(a)-(c). The spatial location for each of these replicatedinput rates are randomly assigned by the statistical model in systemlogic 104 within the test plot in order to provide valid data forsubsequent statistical analysis.

Once the number of replicates and the target input rates are entered bythe user in user interface 102, the user can define in each managementzone the location(s) of test plots 212. For optimal results, anindividual test plot 212 is fully contained within a single managementzone so that testing is conducted in a relatively homogenous sub-fieldtype to minimize variation in other factors beyond the tested treatmentlevels. As part of this process, minimum test plot area needs to bedetermined. The capabilities of machine(s) 220 intended to deliver Aginputs 114 or treatments as well as harvest the output are considered aspart of this process. Such constraints of machine 220 can be stored incentralized database 116 and obtained either from the manufacturer asexternal data 108 or provided by the user at user interface 102.

System logic 104 calculates the time and distance required for machine220 to do a rate change or turn on/off an application of Ag input 114based on constraints of machine 220 and/or its dispensing system. Theconstraints can be one or more of a minimum rate of change in thedispensement of Ag input 114, an operating speed of the dispensingsystem on machine 220, a number of rows (or nozzles) the dispensingsystem on machine 220 can independently control, a volume of Ag input114 that the dispensing system on machine 220 can independently control,and/or sensing capabilities of the intended harvest operation. All ofthis accumulates into system logic 104 defining a minimum individualtreatment area. An individual area for test plot 212 is determined bymultiplying the minimum individual treatment area by the number oftreatment levels as well as how many times each treatment level is to bereplicated. The layout of the area for test plot 212 is done withrespect to the intended travel path through agricultural farm field 214based on Ag field 202. The statistical modeling performed in systemlogic 104 randomly assigns the spatial location of each treatment levelreplicate (individual treatment areas) of Ag inputs 114 within anindividual test plot 212.

FIG. 8A shows a test plot 212(a) with three treatment rates 230, 232,and 234 for Ag input 114 each replicated three or more times within testplot 212(a). System logic 104 has calculated the time and distancerequired for machine 220 to do the rate change, determined the minimumtreatment area required, and laid out the area for test plot 212(a) withrespect to an intended path of travel (in this case 10 degrees off ofdue North) and randomly assigned the spatial location of each treatmentlevel replicate (individual treatment areas) of Ag input 114 withinindividual test plot 212(a). A machine setup file 120 is created for theentire Ag field (including all test plots) and provided by system 100 asexternal data 108. FIG. 7 shows where in user interface 102 the userinitiates the action necessary to create machine setup file 120.

FIG. 8 shows agricultural farm field 214 that corresponds to Ag field202 that was uploaded into system 100. Agricultural farm field 214 hasthree management zones 216, 217, and 218. Management zone 216 has twodefined test plots 212(a) and 212(b) and management zone 218 has onedefined test plot 212(c). Machine setup file 120 is loaded into acontroller 300 onboard machine 220, which also receives GPS locationinformation from satellite 221. Machine 220 has controller 300operatively connected to a dispensing system that is configured tochange the dispensement of Ag input 114 from the dispensing system in apredetermined manner within each predefined test plots 212 a and 212 bin the management zone 216. Machine 220 travels through agriculturalfarm field 214 placing Ag inputs 114 in the typical manner until itreaches test plots 212(a)-(c) where it applies the Ag inputs 114 aspreviously defined. Machine 220 can be any agricultural machine with adispensing system for applying Ag inputs 114, for example, tractors,planters, air seeders, sprayers (ground-based or airplane), irrigationequipment, tillage equipment (agronomic output impact on differentmachine setting levels), harvesters (agronomic output impact ondifferent machine setting levels), etc.

The foregoing automates the process to provide randomized and replicatedpredefined treatment levels within test plots 212 within managementzones 216-218 in agricultural farm field 214 to create datasets ofagronomic response to tested treatment levels that are appropriate foruse in well-established and universally recognized statistical analyses.A simple example could include analyzing three different seeding ratesin a management zone 216, 217, or 218 at the same time as threedifferent nitrogen levels (i.e., 3×3=9 unique treatment levels). If eachpermutation is replicated three times, there will need to betwenty-seven randomly placed treatment areas within a single test plot212. The data and/or statistical analysis output from test plot 212 canalso be aggregated and compared on a regional level with results fromother test plots with and without similar background conditions (e.g.,different management zone “makeup”). This allows growers and theiragronomic advisors, for example, to account for growing season weatherthat may vary between test plots in a region or between differentgrowing seasons/years as well as other factors that may vary within atest plot, such as soil fertility levels, or factors that may varybetween test plots like corn hybrid or seeding rates.

It is important for machine 220 being used to monitor the dispensementof Ag input 114 to generate an application record to associate anintended location for the predetermined change of the dispensement rateof Ag input 114 with an actual location for the predetermined change ofthe dispensement rate of the Ag input 114, in order to confirmsuccessful execution of the treatment levels of Ag input 114 in eachtest plot 212. During harvest, yield data (e.g., volume, moisture,quality attributes like protein) can be observed and recorded formultiple locations in each test plot 212 in an automated fashion usingsensor technology on the harvesting equipment. Such harvest observationscan be automatically sent to centralized database 116 via wirelessconnection or provided by user through user interface 102 of system 100.Each individual yield observation from the harvester is spatially“matched” to the respective actual treatment rate observation. Theresulting data set (along with other agronomic attributes related to thearea for test plot 212) can be provided to the statistical model ofsystem logic 104 of system 100 and utilized in various statisticalanalysis procedures to determine if there is a significant difference inagronomic response between the evaluated treatment rates. This allowsgrowers and agronomic advisors to easily execute test plots 212 thatcomply with scientific experimental design criteria, and leverage theresulting data sets to analyze yield responses to different levels of Aginputs 114 using data that is appropriate for various statisticalanalyses.

Machines 220 can be planters or applicators, which can apply multipleplant rows in a single pass across agricultural farm field 214.Controller on machine 220 implementing instructions on machine setupfile 120 can change the application rate for Ag input 114 as machine 220crosses the field, in some cases within the span of machine 220; forexample, the seeding application rate in adjacent rows can be varied ina test plot 212(a)-(c). At harvest time, machine 220 in the form of aharvester can collect yield data to determine the yield across the widthof the plant collection platform (“header”). It is important to ensurethe width of application control spatially corresponds to the harvestwidth (as currently that is a constraint on the spatial resolution ofmeasuring agronomic output). As part of the determination of the minimumtreatment area required system logic 104 also accounts for thetime/distance required for input rate changes as well as thetime/distance required for the agricultural output (i.e., “crop yield”)to flow through a harvester and pass the agronomic output attributesensor. These parameters or constraints of machine 220 need to beaccounted for when designing test plots 212 to increase the chance ofsuccessful implementation of measuring the agronomic response todifferent treatment rates within test plots 212. The resulting data isextremely valuable for growers and their agronomic advisors.

With this data collected, growers and their agronomic advisors canidentify statistically significant agronomic responses to differenttested treatment levels and determine the confidence level(s) for theanalyses as well as incorporate the differences in cost for eachtreatment level and the per unit value of the Ag output (e.g.,bushels/acre or kilograms/hectare and/or protein level).

Another use case is to evaluate the agronomic impact of settings ofmachine 220 that cannot be adjusted automatically in agricultural farmfield 214. The layout of the test plots 212 will vary from what has beendescribed—essentially doing replicated and randomized strips through thefield with the different settings (e.g., tillage depth, planting depth).It is important to note the resulting analysis of yield observations(and documentation from the application) would again focus on specificmanagement zone areas (relatively homogenous sub-field areas whichminimize variation in other agronomic factors beyond the testedtreatment levels). Accounting for the sensing capabilities of theharvest operation is also important in the design of test plots 212.

FIG. 9 shows an exemplary controller 300 that can be connected to acontroller area network bus (bus) 320 in machine 220. In oneimplementation, controller 300 includes a processor 302, a system memory304, external network interfaces 306 and one or more softwareapplications and drivers enabling or implementing the functionsdescribed herein. External network interface 306 connects GPSinformation data to bus 320 of controller 300. The hardware systemincludes a standard I/O bus 308 with I/O Ports 310 and mass storage 312coupled thereto to store machine setup file 120. Host bridge 316 couplesprocessor 302 to I/O bus 308. The hardware system may further includevideo memory and a display device coupled to the video memory.Collectively, these elements are intended to represent a broad categoryof computer hardware systems, including but not limited to generalpurpose computer systems based on the Pentium processor manufactured byIntel Corporation of Santa Clara, Calif., as well as any other suitableprocessor.

Elements of the computer hardware system perform their conventionalfunctions known in the art. Mass storage 312 is used to providepermanent storage for the data and programming instructions to performthe above-described functions of controlling machine 220, whereas systemmemory 304 (e.g., DRAM) is used to provide temporary storage for thedata and programming instructions when executed by processor 302. I/Oports 310 are one or more serial and/or parallel communication portsused to provide communication between additional peripheral devices likethe control/sensing systems on the dispensing system attached to machine220, which may be coupled to hardware to receive data from sensors.Additionally, machine 220 can have a CAN BUS network to facilitatecommunication on machine 220, or between machine 220 and dispensingsystem—allowing control of electronically controlled items as well asrecording of feedback from sensor systems (e.g., seeding rate on anindividual row).

Controller 300 may include a variety of system architectures, andvarious components of controller 300 may be rearranged. For example,cache 314 may be on-chip with processor 302. Alternatively, cache 314and processor 302 may be packed together as a “processor module,” withprocessor 302 being referred to as the “processor core.” Furthermore,certain implementations of the claimed embodiments may not require norinclude all the above components. For example, additional components maybe included controller 300, such as additional processors, storagedevices, or memories.

While the present invention has been particularly shown and describedwith reference to exemplary embodiments thereof, it should be understoodby those of ordinary skill in the art that various changes,substitutions and alterations can be made herein without departing fromthe scope of the invention as defined by the appended claims and theirequivalents.

1-19. (canceled)
 20. A method comprising: identifying an intendedtreatment application machine and an intended harvester; determining,based on a constraint of the treatment application machine, time ordistance required for the treatment application machine to conduct aninput rate dispensement change; determining a time or distance requiredfor an agricultural output to flow through the harvester; determining,based on the determined time or distance for input rate dispensementchange and time or distance required for the agricultural output to flowthrough the harvester, a minimum treatment area; determining anindividual area for a test plot by multiplying the minimum treatmentarea by a predetermined number of treatment levels and a number of timesthe predetermined number of treatment levels is to be replicated;determining an intended travel path for the identified intendedtreatment application machine through a field; determining a layout ofthe test plot in the field based on the intended path for the identifiedintended treatment application machine; and assigning, randomly, aspatial location of each of the treatment levels to be replicated. 21.The method of claim 20, wherein the constraint of the treatmentapplication machine comprises one or more of a minimum rate of change inthe input rate dispensement, an operating speed of a dispensing systemon the treatment application machine, a number of rows a dispensingsystem on the treatment application machine can independently control,and a volume of input that the dispensing system on the treatmentapplication machine can independently control.
 22. The method of claim20, further comprising collecting yield data, via the harvester.
 23. Themethod of claim 22, further comprising determining crop yield or anagronomic output attribute based on the collected yield data thatmeasures an agronomic response to at least one of the treatment levels.24. The method of claim 23, wherein an application control of thetreatment application machine corresponds spatially to a harvest widthof the harvester.
 25. The method of claim 20, wherein the determiningthe minimum treatment area is based on a constraint of the harvestercomprising a harvest width of the harvester.
 26. The method of claim 25,wherein the harvest width is sufficient for crop yield or an agronomicoutput attribute to be measured.
 27. The method of claim 20, wherein thepredetermined number of treatment levels is two.
 28. The method of claim20, wherein the predetermined number of treatment levels is three. 29.The method of claim 20, wherein the number of times the predeterminednumber of treatment levels is to be replicated is three.
 30. The methodof claim 20, wherein the number of times the predetermined number oftreatment levels is to be replicated is five.
 31. The method of claim20, generating an application record of an actual location for thedispensement in the determined individual area for the test plot. 32.The method of claim 20, associating the determined spatial location ofeach of the treatment levels with the actual location for thedispensement.
 33. The method of claim 20, further comprising observing,during harvest, yield data.
 34. The method of claim 33, furthercomprising matching the observed yield data with a respective actualtreatment rate.
 35. The method of claim 33, further comprisingdetermining, based on the yield data, a difference in agronomic responseof a plurality of the treatment rates using statistical modeling. 36.The method of claim 20, further comprising providing instructions to theintended application machine for following a plurality of plantingpoints and changing the dispensement base on treatment levels in thetest plot.
 37. The method of claim 36, wherein the instructions arechosen from at least one of tillage depth, planting depth, tillageangle, residue spread width, amendment, seed hybrid, number of seeds perarea, weight of seeds per area, volume, or weight.
 38. The method ofclaim 20, further comprising displaying the field, or a portion thereof,on a user interface.